{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Calling germline variants in amplicon-based resequencing data\n",
    "\n",
    "This tutorial is based on [De novo and inherited loss-of-function\n",
    "variants in *TLK2*: identification, clinical delineation and\n",
    "genotype-phenotype evaluation of a distinct neurodevelopmental disorder](https://www.sciencedirect.com/science/article/pii/S0002929718301617)\n",
    "by MRF Reijnders, KA Miller et al.\n",
    "The experimental data used here was generated by KA Miller.\n",
    "\n",
    "After identifying a possibly pathogenic mutation in *TLK2*, we wanted to\n",
    "screen a large cohort of patients for anybody else who carries a\n",
    "mutation in that gene. We designed primers to capture exons 2-22 and\n",
    "used a 384-sample paired-end kit to resequence 384 patients in a single\n",
    "Illumina MiSeq run.\n",
    "\n",
    "In this tutorial, we will look at how the data from this experiment was processed\n",
    "using amplimap. We will be working with a subset of three samples, to speed\n",
    "up processing times.\n",
    "\n",
    "## Analysis overview\n",
    "\n",
    "Starting from the raw sequencing reads, we would like to:\n",
    "\n",
    "-  Trim off primer sequences\n",
    "-  Align reads to the reference genome\n",
    "-  Determine whether any target regions may have not been adequately\n",
    "   covered in some of the samples\n",
    "-  Call germline variants\n",
    "-  Annotate variants with their consequences as well as allele\n",
    "   frequencies and deleteriousness scores\n",
    "-  Generate an overview table for manual inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial setup\n",
    "\n",
    "To run this tutorial amplimap needs to be installed and configured already.\n",
    "Please see [Installation](https://amplimap.readthedocs.io/en/latest/installation.html)\n",
    "and [Configuration](https://amplimap.readthedocs.io/en/latest/configuration.html) for details.\n",
    "\n",
    "In particular, you need to have the hg19 (GRCh37) reference FASTA genome and the\n",
    "associated indices prepared for use with your default aligner (see [Reference genome paths](https://amplimap.readthedocs.io/en/latest/configuration.html#reference-genome-paths)).\n",
    "\n",
    "In addition, you need to have [Annovar installed and your indices set up](https://amplimap.readthedocs.io/en/latest/configuration.html#setting-up-annovar)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the working directory\n",
    "\n",
    "\n",
    "For every experiment that we want to process, we create a new working\n",
    "directory. This will contain all the input files required, as well as\n",
    "the output generated by amplimap. This makes it easy to keep track of\n",
    "the data for each experiment, as well as to rerun analyses if required.\n",
    "\n",
    "To create a directory, we use the standard ``mkdir`` unix command and\n",
    "change into it with ``cd``:\n",
    "\n",
    "    mkdir TLK2\n",
    "    cd TLK2\n",
    "\n",
    "All further commands should now be run inside this working directory.\n",
    "\n",
    "### reads_in\n",
    "\n",
    "\n",
    "The first input we need to provide to amplimap is of course the\n",
    "sequencing data. These can be obtained directly from the sequencer as\n",
    "``.fastq.gz`` files and should be placed in a directory called [``reads_in``](https://amplimap.readthedocs.io/en/latest/usage.html#reads-in).\n",
    "\n",
    "[Download the sample data from this tutorial](http://userweb.molbiol.ox.ac.uk/public/koelling/amplimap/tutorial_data/TLK2.tar)\n",
    "and extract the ``reads_in`` directory into your working directory. There are many different ways of doing this but\n",
    "we recommend using ``wget`` and ``tar`` on the command line:\n",
    "\n",
    "    wget http://userweb.molbiol.ox.ac.uk/public/koelling/amplimap/tutorial_data/TLK2.tar\n",
    "    tar xf TLK2.tar\n",
    "\n",
    "You can use ``ls`` to check that the files have been extracted to the correct subdirectory:\n",
    "\n",
    "    ls reads_in\n",
    "\n",
    "This should display a list of six fastq.gz files, which represent read 1\n",
    "and read 2 of three samples:\n",
    "\n",
    "    Sample1_L001_R1_001.fastq.gz  Sample1_L001_R2_001.fastq.gz\n",
    "    Sample2_L001_R1_001.fastq.gz  Sample2_L001_R2_001.fastq.gz\n",
    "    Sample3_L001_R1_001.fastq.gz  Sample3_L001_R2_001.fastq.gz\n",
    "\n",
    "\n",
    "\n",
    "### probes.csv\n",
    "\n",
    "Next, we need to provide a [probes.csv file](https://amplimap.readthedocs.io/en/latest/usage.html#probes-csv) that describes the used\n",
    "primer sequences and the regions they are supposed to capture. This can\n",
    "be created with spreadsheet software such as Excel, as long as the file is\n",
    "saved as plain text. However, we recommend always checking the file manually\n",
    "using a plain text editor such as ``nano`` or ``vim``, to make sure it is actually in the right\n",
    "format.\n",
    "\n",
    "Create a new plain text file called ``probes.csv`` (for example using ``nano``\n",
    "or ``vim``) in your working directory and copy the following text into it:\n",
    "\n",
    "    id,first_primer_5to3,second_primer_5to3,chr,target_start,target_end,strand\n",
    "    TLK2-Ex2,AATTACTGTGAGTTTTGTTCTACAG,ACTATGTTAAATGACTACTGGAATGACC,chr17,60558422,60558714,+\n",
    "    TLK2-Ex3,ACGCCATTGTATTCCAGCCGGGGTGAT,CAGCCTTGAGCCACCAAACCTGGCCCAAAC,chr17,60598019,60598245,+\n",
    "    TLK2-Ex4,TAAGAGGAAGACAGTGATTCAGGAC,GAACTAACACTGTTCTGTCAGGTG,chr17,60599449,60599800,+\n",
    "    TLK2-Ex5,TGGAGGAAATAGTCTGTTCTTG,ATGTTGCCCAGGTTGGCCTCGAACT,chr17,60600344,60600732,+\n",
    "    TLK2-Ex6,GCATAGTACTGTTTTGAATTATTCATATCG,CTCTTCTGTAAAAAGCTAATTTACTGAC,chr17,60601577,60601855,+\n",
    "    TLK2-Ex7,CTTATATTTGATAACTGTTTTTAACCCG,GAGCACTAGGGCAATGGAAAGGATA,chr17,60613463,60613723,+\n",
    "    TLK2-Ex8,GAACTTGGTATAAACCACCATGTCC,GTGGTCAGAGAAATACAGAGAAGTC,chr17,60629478,60629842,+\n",
    "    TLK2-Ex9,ATTTGTGTGAGCAAGTGCTTTTTCC,GGTGCTTGCTATAAAATCTCTTACA,chr17,60630934,60631128,+\n",
    "    TLK2-Ex10,AAACATGCCCAAATTAGTAATTCAA,CAAATCATGTTCCTAAAAAGCTCTAC,chr17,60637164,60637585,+\n",
    "    TLK2-Ex11,TTCTAAGAAGTGTCTTTATCCATGC,AGGACTTCACCTCATTCGATAC,chr17,60642320,60642640,+\n",
    "    TLK2-Ex12,AAATTGGATACACAAGTGACAAATTG,CTATTGCCGGTGACAATCAAC,chr17,60650431,60650833,+\n",
    "    TLK2-Ex13,GCTTTGAAGTTCTTCCCTCACATC,CACTGAAGCTTTCTGCTGCTATG,chr17,60653891,60654235,+\n",
    "    TLK2-Ex14,TTACTGAACTCCTCTGTATGGTTTG,AGCAATCTCCAACCCAATATGC,chr17,60655659,60655949,+\n",
    "    TLK2-Ex15,CTGGGAATTTTGCAAGCGTGG,TATGAGGCAGGAAGTACAGAACC,chr17,60657421,60657827,+\n",
    "    TLK2-Ex16,TAATCACAAGTTTCAAGAAGGTGCT,ACCAACAACAATGCACGTAAAG,chr17,60663354,60663656,+\n",
    "    TLK2-Ex17,TCTCAATGGCTTGGTAGATTCC,TGTCACAAATTACTTGGTTCCCTC,chr17,60673772,60674134,+\n",
    "    TLK2-Ex18,AGGTAGTGTTAATCTGCTTGCTC,TCCAACACGCCCTCCTAAAC,chr17,60677974,60678351,+\n",
    "    TLK2-Ex19,AGTCCAGATTGCTTGATTCCC,GCCACATCTCTATAGCCAACCTG,chr17,60679277,60679561,+\n",
    "    TLK2-Ex20,GTACATGTCTTAAACTTATATGATC,CCTAGGGTTGAGGATTTCTGCT,chr17,60683454,60683774,+\n",
    "    TLK2-Ex21,CCCACTCTGCTTTGACCTGGTAG,TTCACACTGAAGAATCCATCCA,chr17,60685195,60685580,+\n",
    "    TLK2-Ex22,AGAGGTACTTCTGTTGGTGCTT,GGATTCGCTATGTTCCAAACC,chr17,60689581,60690988,+\n",
    "\n",
    "### targets.csv\n",
    "\n",
    "We also want to describe the target regions that amplimap\n",
    "should analyse in the [targets.csv](https://amplimap.readthedocs.io/en/latest/usage.html#targets-csv) file. This helps speed up the\n",
    "process, since any reads outside the targets can be ignored. It also\n",
    "allows us to calculate the average coverage for each target in each\n",
    "sample.\n",
    "\n",
    "Create a new plain text file called ``targets.csv`` (for example using ``nano``\n",
    "or ``vim``) in your working directory and copy the following text into it:\n",
    "\n",
    "    chr,start,end,id\n",
    "    chr17,60558422,60558714,TLK2-Ex2\n",
    "    chr17,60598019,60598245,TLK2-Ex3\n",
    "    chr17,60599449,60599800,TLK2-Ex4\n",
    "    chr17,60600344,60600732,TLK2-Ex5\n",
    "    chr17,60601577,60601855,TLK2-Ex6\n",
    "    chr17,60613463,60613723,TLK2-Ex7\n",
    "    chr17,60629478,60629842,TLK2-Ex8\n",
    "    chr17,60630934,60631128,TLK2-Ex9\n",
    "    chr17,60637164,60637585,TLK2-Ex10\n",
    "    chr17,60642320,60642640,TLK2-Ex11\n",
    "    chr17,60650508,60650785,TLK2-Ex12\n",
    "    chr17,60653891,60654235,TLK2-Ex13\n",
    "    chr17,60655659,60655949,TLK2-Ex14\n",
    "    chr17,60657421,60657827,TLK2-Ex15\n",
    "    chr17,60663354,60663656,TLK2-Ex16\n",
    "    chr17,60673772,60674134,TLK2-Ex17\n",
    "    chr17,60677974,60678351,TLK2-Ex18\n",
    "    chr17,60679277,60679561,TLK2-Ex19\n",
    "    chr17,60683454,60683774,TLK2-Ex20\n",
    "    chr17,60685195,60685580,TLK2-Ex21\n",
    "    chr17,60689581,60689989,TLK2-Ex22\n",
    "\n",
    "### config.yaml\n",
    "\n",
    "Finally, we create a config.yaml file to set some experiment-specific settings.\n",
    "We could set [a lot more options](https://amplimap.readthedocs.io/en/latest/configuration.html)\n",
    "here but in this case set a few of them. All other options will be left\n",
    "as specified in the default configuration.\n",
    "\n",
    "Create a new plain text file called ``config.yaml`` (for example using ``nano``\n",
    "or ``vim``) in your working directory and copy the following text into it:\n",
    "\n",
    "    general:\n",
    "      genome_name: \"hg19\"\n",
    "\n",
    "This tells amplimap to use the reference genome ``hg19``, as specified in your\n",
    "[default configuration](https://amplimap.readthedocs.io/en/latest/configuration.html#default-configuration).\n",
    "If you do not have this reference genome set up there, you can also specify the necessary paths directly\n",
    "in the ``config.yaml`` by adding the following additional lines and editing the paths to match your local setup:\n",
    "\n",
    "    paths:\n",
    "      hg19:\n",
    "        bwa: \"/INSERT/PATH/TO/PREFIX\"\n",
    "        fasta: \"/INSERT/PATH/TO/FASTA\"\n",
    "        annovar: \"/INSERT/PATH/TO/ANNOVAR/INDICES\"\n",
    "        \n",
    "For ``bwa`` you would provide the path of the prefix you used when [building the BWA index](http://bio-bwa.sourceforge.net/bwa.shtml#3).\n",
    "If you are using Bowtie2 as your aligner you can replace ``bwa`` with ``bowtie2`` in the config file and provide the prefix of its index files instead.\n",
    "For ``fasta`` you would provide the path to the corresponding FASTA file, which needs to have been indexed with ``samtools faidx``.\n",
    "Finally, you need to provide the path to your Annovar index directory under ``annovar``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running amplimap\n",
    "\n",
    "Now we can run amplimap. In our case, we want to obtain coverage values\n",
    "(“coverages”) and annotated variant calls (“variants”). This will also\n",
    "automatically run the other parts of the pipeline that are required,\n",
    "such as trimming the primers and aligning reads to the genome.\n",
    "First we will do a dry-run to confirm that all input files can be found:\n",
    "\n",
    "    amplimap coverages variants\n",
    "    \n",
    "This should output a long list of commands, ending with these lines:\n",
    "\n",
    "    Job counts:\n",
    "        count   jobs\n",
    "        3       align_pe\n",
    "        3       annotate_variants\n",
    "        3       calc_coverage\n",
    "        3       call_variants_raw\n",
    "        1       convert_targets_csv\n",
    "        1       copy_probes\n",
    "        1       coverage_agg\n",
    "        3       coverage_process\n",
    "        1       coverages\n",
    "        6       link_reads\n",
    "        1       merge_targets\n",
    "        3       parse_reads_pe\n",
    "        1       start_analysis\n",
    "        3       stats_alignment\n",
    "        1       stats_alignment_agg\n",
    "        1       stats_reads_agg\n",
    "        1       stats_samples_agg\n",
    "        6       tool_version\n",
    "        1       variants\n",
    "        1       variants_merge\n",
    "        1       variants_summary\n",
    "        1       variants_summary_condensed\n",
    "        46\n",
    "\n",
    "    amplimap dry run successful. Set --run to run!\n",
    "    \n",
    "You can see how amplimap is planning to run 3 alignment jobs (align_pe) and 3 variant calling jobs (call_variants),\n",
    "corresponding to the 3 samples we are analysing.\n",
    "\n",
    "Having confirmed that everything looks as expected, we can run amplimap:\n",
    "\n",
    "    amplimap coverages variants --run\n",
    "\n",
    "This will take a few minutes to complete. It would be much faster if we\n",
    "ran jobs in parallel (for example using a cluster), but we are not\n",
    "doing that for the purposes of this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing the results\n",
    "\n",
    "amplimap has now processed our reads, aligned them to the reference genome, called germline variants, annotated them\n",
    "and produced a summary table with the variant calls.\n",
    "All of the output files have been placed into the ``analysis`` directory.\n",
    "\n",
    "Let's explore some of the output. Most analyses in amplimap produce one or more CSV file with a table of results. In this tutorial, we will use Python and pandas to process and visualize these files. However, the same thing could also be done in R or Excel.\n",
    "\n",
    "### analysis/reads_parsed/\n",
    "\n",
    "This directory contains results from the first step of the pipeline which\n",
    "identified primer arms in reads, trimmed them off and calculated some\n",
    "run statistics.\n",
    "\n",
    "For example, the ``stats_samples.csv`` file tells us about the number of reads in each sample and how many of these contained the expected primer sequences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>files</th>\n",
       "      <th>pairs_total</th>\n",
       "      <th>pairs_unknown_arms</th>\n",
       "      <th>pairs_good_arms</th>\n",
       "      <th>pairs_r1_too_short</th>\n",
       "      <th>pairs_r2_too_short</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>1</td>\n",
       "      <td>29218</td>\n",
       "      <td>2962</td>\n",
       "      <td>26256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sample2</td>\n",
       "      <td>1</td>\n",
       "      <td>6887</td>\n",
       "      <td>859</td>\n",
       "      <td>6028</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sample3</td>\n",
       "      <td>1</td>\n",
       "      <td>19476</td>\n",
       "      <td>1918</td>\n",
       "      <td>17558</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample  files  pairs_total  pairs_unknown_arms  pairs_good_arms  \\\n",
       "0  Sample1      1        29218                2962            26256   \n",
       "1  Sample2      1         6887                 859             6028   \n",
       "2  Sample3      1        19476                1918            17558   \n",
       "\n",
       "   pairs_r1_too_short  pairs_r2_too_short  \n",
       "0                   0                   0  \n",
       "1                   0                   0  \n",
       "2                   0                   0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "pd.read_csv('analysis/reads_parsed/stats_samples.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for a more detailed look at the number of reads observed per probe in each sample, there is ``stats_reads.csv``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>probe</th>\n",
       "      <th>read_pairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex10</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex11</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex12</td>\n",
       "      <td>3675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex13</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex14</td>\n",
       "      <td>616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample      probe  read_pairs\n",
       "0  Sample1  TLK2-Ex10         130\n",
       "1  Sample1  TLK2-Ex11         719\n",
       "2  Sample1  TLK2-Ex12        3675\n",
       "3  Sample1  TLK2-Ex13        1152\n",
       "4  Sample1  TLK2-Ex14         616"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('analysis/reads_parsed/stats_reads.csv').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table already gives us an idea of how well we covered each of the exons in each of the samples. However, the more accurate way of looking at this is through the coverage tables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analysis/bams/coverages/\n",
    "\n",
    "This directory contains the coverage information for each sample and each target.\n",
    "The most comprehensive table to look at is ``coverages_long.csv``. In addition,\n",
    "there are also various aggregate tables that, for example, tell you the minimum coverage in each target and\n",
    "each sample (``min_coverage.csv``) or what fraction of the target was not covered by any reads (``fraction_zero_coverage.csv``).\n",
    "\n",
    "Let's load the ``fraction_zero_coverage.csv`` file and visualize the zero coverage fractions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Fraction of exon with no coverage')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wEGpSO7Ypz9QFxgfHqVreAl6gQVPPccpA5Uf3zOz5VmRQQoFx08UeHKdMDIQ4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\n",
      "text/plain": [
       "<Figure size 252x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zero_fraction = pd.read_csv('analysis/bams/coverages/fraction_zero_coverage.csv', index_col='Target')\n",
    "\n",
    "#reorder by exon number\n",
    "zero_fraction['sort_key'] = zero_fraction.index.str.replace('TLK2-Ex', '').astype(int)\n",
    "zero_fraction = zero_fraction.sort_values('sort_key').drop(columns=['sort_key'])\n",
    "\n",
    "plt.figure(figsize=(3.5, 5))\n",
    "sns.heatmap(zero_fraction, cmap='RdYlGn_r', linewidths=0.1)\n",
    "plt.title(\"Fraction of exon with no coverage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that there is one exon that had very low coverage in one of the targets. To list all targets with less than 95% coverage in at least one sample, we can use this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample1</th>\n",
       "      <th>Sample2</th>\n",
       "      <th>Sample3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TLK2-Ex15</th>\n",
       "      <td>0.002457</td>\n",
       "      <td>0.002457</td>\n",
       "      <td>0.457002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sample1   Sample2   Sample3\n",
       "Target                                 \n",
       "TLK2-Ex15  0.002457  0.002457  0.457002"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_fraction[zero_fraction.apply(max, axis = 1) > 0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, almost half of exon 15 (45.7%) was not covered at all in sample 3. We might want to try sequencing this sample again to make sure we didn't miss any mutations in this exon. Otherwise, most exons seem to have been covered quite well in all samples, as indicated by the average coverage shown in the ``cov_per_bp.csv`` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample1</th>\n",
       "      <th>Sample2</th>\n",
       "      <th>Sample3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TLK2-Ex10</th>\n",
       "      <td>140.907583</td>\n",
       "      <td>29.992891</td>\n",
       "      <td>9.767773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLK2-Ex11</th>\n",
       "      <td>1034.809969</td>\n",
       "      <td>245.526480</td>\n",
       "      <td>1259.570093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLK2-Ex12</th>\n",
       "      <td>6126.651079</td>\n",
       "      <td>1915.147482</td>\n",
       "      <td>5215.931655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLK2-Ex13</th>\n",
       "      <td>1542.437681</td>\n",
       "      <td>282.860870</td>\n",
       "      <td>1395.542029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLK2-Ex14</th>\n",
       "      <td>979.910653</td>\n",
       "      <td>273.639175</td>\n",
       "      <td>909.109966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sample1      Sample2      Sample3\n",
       "Target                                          \n",
       "TLK2-Ex10   140.907583    29.992891     9.767773\n",
       "TLK2-Ex11  1034.809969   245.526480  1259.570093\n",
       "TLK2-Ex12  6126.651079  1915.147482  5215.931655\n",
       "TLK2-Ex13  1542.437681   282.860870  1395.542029\n",
       "TLK2-Ex14   979.910653   273.639175   909.109966"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_per_bp = pd.read_csv('analysis/bams/coverages/cov_per_bp.csv', index_col='Target')\n",
    "cov_per_bp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average coverage for exon 10 was only around 10x in sample 3, but apart from exon 10 and 15 all other exons were covered to at least ~70x in all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target\n",
       "TLK2-Ex10       9.767773\n",
       "TLK2-Ex11     245.526480\n",
       "TLK2-Ex12    1915.147482\n",
       "TLK2-Ex13     282.860870\n",
       "TLK2-Ex14     273.639175\n",
       "TLK2-Ex15       0.597052\n",
       "TLK2-Ex16      69.904290\n",
       "TLK2-Ex17     254.435262\n",
       "TLK2-Ex18     234.023810\n",
       "TLK2-Ex19     302.389474\n",
       "TLK2-Ex2      611.010239\n",
       "TLK2-Ex20    1096.919003\n",
       "TLK2-Ex21     102.580311\n",
       "TLK2-Ex22     285.767726\n",
       "TLK2-Ex3      353.383260\n",
       "TLK2-Ex4      136.312500\n",
       "TLK2-Ex5       84.308483\n",
       "TLK2-Ex6      222.559140\n",
       "TLK2-Ex7     1532.881226\n",
       "TLK2-Ex8      207.038356\n",
       "TLK2-Ex9      189.302564\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_per_bp.apply(min, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analysis/variants_raw/\n",
    "\n",
    "Here we can find the final output of the germline variant calling -\n",
    "the annotated table of variants. The ``variants_summary.csv``\n",
    "table includes all the information one would usually find in a VCF\n",
    "file (chromosome, position, ref, alt, genotype, allele fraction, quality),\n",
    "as well as various annotations from Annovar regarding the functional\n",
    "impact and deleteriousness of the variant. There is also information\n",
    "about the frequency of the variant in reference sets, such as 1000 Genomes,\n",
    "gnomAD or ExAC.\n",
    "\n",
    "There will be one line per sample and variant, so the same variant can appear\n",
    "in multiple lines.\n",
    "\n",
    "Let's have a look at a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Target</th>\n",
       "      <th>Var_Zygosity</th>\n",
       "      <th>Var_AltFraction</th>\n",
       "      <th>Var_FailedFilters</th>\n",
       "      <th>Func.refGene</th>\n",
       "      <th>ExonicFunc.refGene</th>\n",
       "      <th>ExAC_ALL</th>\n",
       "      <th>gnomAD_genome_ALL</th>\n",
       "      <th>CADD_phred</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Ref</th>\n",
       "      <th>Alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex3</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.301471</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60598038</td>\n",
       "      <td>AAA</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex4</td>\n",
       "      <td>HOM</td>\n",
       "      <td>1.000000</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.9611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60599797</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex5</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.507042</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60600729</td>\n",
       "      <td>C</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex10</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.489796</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60637258</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex20</td>\n",
       "      <td>HOM</td>\n",
       "      <td>1.000000</td>\n",
       "      <td></td>\n",
       "      <td>exonic</td>\n",
       "      <td>synonymous SNV</td>\n",
       "      <td>0.5926</td>\n",
       "      <td>0.5195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60683503</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sample     Target Var_Zygosity  Var_AltFraction Var_FailedFilters  \\\n",
       "0  Sample1   TLK2-Ex3          Het         0.301471                     \n",
       "1  Sample1   TLK2-Ex4          HOM         1.000000                     \n",
       "2  Sample1   TLK2-Ex5          Het         0.507042                     \n",
       "3  Sample1  TLK2-Ex10          Het         0.489796                     \n",
       "4  Sample1  TLK2-Ex20          HOM         1.000000                     \n",
       "\n",
       "  Func.refGene ExonicFunc.refGene  ExAC_ALL  gnomAD_genome_ALL  CADD_phred  \\\n",
       "0     intronic                       0.0000             0.0003         0.0   \n",
       "1     intronic                       0.0000             0.9611         0.0   \n",
       "2     intronic                       0.0000             0.0952         0.0   \n",
       "3     intronic                       0.0000             0.1359         0.0   \n",
       "4       exonic     synonymous SNV    0.5926             0.5195         0.0   \n",
       "\n",
       "     Chr     Start  Ref Alt  \n",
       "0  chr17  60598038  AAA   -  \n",
       "1  chr17  60599797    G   A  \n",
       "2  chr17  60600729    C   G  \n",
       "3  chr17  60637258    C   A  \n",
       "4  chr17  60683503    A   G  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variants = pd.read_csv('analysis/variants_raw/variants_summary.csv')\n",
    "\n",
    "#fill empty cells\n",
    "variants.fillna(value={'Var_FailedFilters': '', 'ExonicFunc.refGene': '', 'ExAC_ALL': 0, 'gnomAD_genome_ALL': 0, 'CADD_phred': 0}, inplace=True)\n",
    "\n",
    "#extract columns\n",
    "variants = variants[ ['Sample', 'Target', 'Var_Zygosity', 'Var_AltFraction', 'Var_FailedFilters', 'Func.refGene', 'ExonicFunc.refGene', 'ExAC_ALL', 'gnomAD_genome_ALL', 'CADD_phred', 'Chr', 'Start', 'Ref', 'Alt'] ]\n",
    "\n",
    "\n",
    "variants.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In total we found 21 variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "print(len(variants))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of these variants seem to appear at high frequencies in the general population, as indicated by the ExAC and gnomAD frequencies. We expect pathogenic mutations in this gene to have full penetrance and know that the disease should be rare, so anything above 1% allele frequency is clearly not of interest. Thus, we filter out common variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Target</th>\n",
       "      <th>Var_Zygosity</th>\n",
       "      <th>Var_AltFraction</th>\n",
       "      <th>Var_FailedFilters</th>\n",
       "      <th>Func.refGene</th>\n",
       "      <th>ExonicFunc.refGene</th>\n",
       "      <th>ExAC_ALL</th>\n",
       "      <th>gnomAD_genome_ALL</th>\n",
       "      <th>CADD_phred</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Ref</th>\n",
       "      <th>Alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample1</td>\n",
       "      <td>TLK2-Ex3</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.301471</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60598038</td>\n",
       "      <td>AAA</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sample2</td>\n",
       "      <td>TLK2-Ex2</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.125000</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60558639</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sample2</td>\n",
       "      <td>TLK2-Ex12</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.200000</td>\n",
       "      <td></td>\n",
       "      <td>exonic</td>\n",
       "      <td>stopgain</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>39.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60650596</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sample3</td>\n",
       "      <td>TLK2-Ex3</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.324138</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60598038</td>\n",
       "      <td>AAA</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sample3</td>\n",
       "      <td>TLK2-Ex5</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.083333</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60600706</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sample3</td>\n",
       "      <td>TLK2-Ex5</td>\n",
       "      <td>Het</td>\n",
       "      <td>0.083333</td>\n",
       "      <td></td>\n",
       "      <td>intronic</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chr17</td>\n",
       "      <td>60600731</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample     Target Var_Zygosity  Var_AltFraction Var_FailedFilters  \\\n",
       "0   Sample1   TLK2-Ex3          Het         0.301471                     \n",
       "6   Sample2   TLK2-Ex2          Het         0.125000                     \n",
       "10  Sample2  TLK2-Ex12          Het         0.200000                     \n",
       "14  Sample3   TLK2-Ex3          Het         0.324138                     \n",
       "16  Sample3   TLK2-Ex5          Het         0.083333                     \n",
       "18  Sample3   TLK2-Ex5          Het         0.083333                     \n",
       "\n",
       "   Func.refGene ExonicFunc.refGene  ExAC_ALL  gnomAD_genome_ALL  CADD_phred  \\\n",
       "0      intronic                          0.0             0.0003         0.0   \n",
       "6      intronic                          0.0             0.0000         0.0   \n",
       "10       exonic           stopgain       0.0             0.0000        39.0   \n",
       "14     intronic                          0.0             0.0003         0.0   \n",
       "16     intronic                          0.0             0.0000         0.0   \n",
       "18     intronic                          0.0             0.0000         0.0   \n",
       "\n",
       "      Chr     Start  Ref Alt  \n",
       "0   chr17  60598038  AAA   -  \n",
       "6   chr17  60558639    A   G  \n",
       "10  chr17  60650596    C   A  \n",
       "14  chr17  60598038  AAA   -  \n",
       "16  chr17  60600706    A   G  \n",
       "18  chr17  60600731    T   G  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variants[ (variants['Var_FailedFilters'] == '') & (variants['ExAC_ALL'] < 0.01) & (variants['gnomAD_genome_ALL'] < 0.01) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see some intronic variant calls that do not very convincing, including one in two unrelated samples. This suggests that this is likely to be either a common polymorphism or a recurrent variant calling error. We might want to filter variants like this out by setting stricter filters, for example on the allele frequency (eg. <0.1%), the alt allele fraction (eg. > 15%) or directly on the number of samples in our screen that carried that mutation (eg. < 3).\n",
    "\n",
    "However, we also found one novel variant in sample 2 that looks very interesting: a stopgain mutation in exon 12 that has never been observed in ExAC or gnomAD, and which is predicted to be highly deleterious by CADD with a score of 39. This would certainly warrant further investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Target</th>\n",
       "      <th>Ref</th>\n",
       "      <th>Alt</th>\n",
       "      <th>Impact</th>\n",
       "      <th>CADD</th>\n",
       "      <th>gnomAD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>TLK2-Ex20</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>synonymous SNV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>TLK2-Ex12</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>stopgain</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>TLK2-Ex20</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>synonymous SNV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>TLK2-Ex20</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>synonymous SNV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID     Target Ref Alt          Impact  CADD  gnomAD\n",
       "4   1  TLK2-Ex20   A   G  synonymous SNV   0.0   51.95\n",
       "10  2  TLK2-Ex12   C   A        stopgain  39.0    0.00\n",
       "12  2  TLK2-Ex20   A   G  synonymous SNV   0.0   51.95\n",
       "19  3  TLK2-Ex20   A   G  synonymous SNV   0.0   51.95"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#simple overview table:\n",
    "variants['ID'] = variants['Sample'].str.replace('Sample', '')\n",
    "variants['gnomAD'] = variants['gnomAD_genome_ALL'] * 100.0\n",
    "variants.rename(columns={'ExonicFunc.refGene': 'Impact', 'CADD_phred': 'CADD'}).loc[\n",
    "    (variants['Var_FailedFilters'] == '') & (variants['Func.refGene'] != 'intronic'),\n",
    "    ['ID', 'Target', 'Ref', 'Alt', 'Impact', 'CADD', 'gnomAD']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(43.4167,0.5,'')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O8y/KRcCVlCmrPafmV3U2oAR/m+fHZwOr97mNLen8XE1KkPLOc9TbntnT2a9u2UbP6XXV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mIecG71e1gmm3YA5gEvrS13UWb+aJzYJEmSJE1kxH0aUjdMOg3G3PKrEwcB83sRRGYurCSeNq48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\nEzoBAAAAMN1/AasiBC8611OQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1050x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#additional figures in high-res\n",
    "plt.figure(figsize=(3.5, 5), dpi=300)\n",
    "sns.heatmap(zero_fraction, cmap='RdYlGn_r', linewidths=0.1)\n",
    "plt.title(\"Fraction of exon with no coverage\")\n",
    "plt.ylabel(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RUbVlp0+fLvOF/quvvsKtW7fU2sbKmjVrJljOyclR6sttbm4uPv30U5XqHjJk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\n9jFTo8S0VmZOjpZ9cdRUUkAgUw74+6Pl9esSxAO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dujY3N1eZmZlG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XFxcRZlCgAAAABoKMuK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snDVr1pjagwcPrrQwc7uBAwfqT3/6k1at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\no1ZaWipJstlsdr2vt7e3Xe8HAADQEFGcAQA0eM2aNZOvr6/VaQA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lqT32tmXL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\n",
      "text/plain": [
       "<Figure size 1050x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read stats\n",
    "stats_reads = pd.read_csv('analysis/reads_parsed/stats_reads.csv')\n",
    "\n",
    "#reorder by exon number\n",
    "stats_reads['sort_key'] = stats_reads['probe'].str.replace('TLK2-Ex', '').astype(int)\n",
    "stats_reads = stats_reads.sort_values('sort_key').drop(columns=['sort_key'])\n",
    "\n",
    "plt.figure(figsize=(3.5, 6), dpi=300)\n",
    "sns.boxplot(y='probe', x='read_pairs', color='white', data=stats_reads, orient='h')\n",
    "sns.despine(bottom=True)\n",
    "plt.title(\"Read pairs per probe\")\n",
    "plt.ylabel(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amplimap 0.4.5\n",
      "_amplimap: 0.4.5\n"
     ]
    }
   ],
   "source": [
    "!amplimap --version\n",
    "!grep '_amplimap' analysis/versions.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
